Abstract

The prediction of various GPCR conformations along their activation pathways has been a challenge for computational
biophysicists. The shortage of exptl. active structures has impaired the study of GPCR activation mechanisms. We have
developed a hybrid computational method that predicts multiple GPCR conformations systematically, including the active ones.
This is one of the very few methods that can predict the high-energy active conformations, capable of coupling to the G
protein, starting from an inactive conformation. We are also able to generate, to the best of our knowledge, the first quant.
energy profile of GPCR activation consistent with the qual. energy landscape from expts. Our hybrid approach starts
with conformational sampling over a large landscape using a coarse grid of helix tilts and rotations in the membrane. It then
selects lowest- energy conformations in the inactive- state and potential active-state energy wells defined by the TM3-TM6
intracellular end distance, which is a simple but reasonable activation coordinate. These conformations are then subjected to
local sampling on a fine grid of helix tilts and rotations. This hierarchical sampling is able to identify high-energy active- state
conformations seen in crystal structures, because those conformations still reside in their local energy wells. The lowest-energy
conformations in each of the distinct energy wells are subjected to mol. dynamics (MD) simulation in explicit
membrane for local relaxation. We have validated the method with β_2 adrenergic (hβ_2AR) and M2 muscarinic acetylcholine
receptors, which have both active- and inactive- state crystal structures available. Interaction energy anal. of MD trajectories is
able to reproduce key features of the qual. energy landscape of hβ_2AR activation presented in exptl. studies [Manglik et al.,
2015, Cell 161, 1101]. We have also applied this methodol. to a GPCR with unknown exptl. structure, the human somatostatin
receptor subtype 5. We are able to identify the agonist- GPCR and Gα-GPCR interactions crit. in its activation, and also
generate a quant. energy profile consistent with exptl. observations that both the agonist and the G protein are needed to
stabilize the active state. These results demonstrate our method's ability to predict the active conformations and the energy
landscape of activation of GPCRs, which provides detailed structural insights into GPCR function.